TY - GEN
T1 - The variational hierarchical EM algorithm for clustering hidden Markov models
AU - Coviello, Emanuele
AU - Chan, Antoni B.
AU - Lanckriet, Gert R.G.
PY - 2012
Y1 - 2012
N2 - In this paper, we derive a novel algorithm to cluster hidden Markov models (HMMs) according to their probability distributions. We propose a variational hierarchical EM algorithm that i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a "cluster center", i.e., a novel HMM that is representative for the group. We illustrate the benefits of the proposed algorithm on hierarchical clustering of motion capture sequences as well as on automatic music tagging.
AB - In this paper, we derive a novel algorithm to cluster hidden Markov models (HMMs) according to their probability distributions. We propose a variational hierarchical EM algorithm that i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a "cluster center", i.e., a novel HMM that is representative for the group. We illustrate the benefits of the proposed algorithm on hierarchical clustering of motion capture sequences as well as on automatic music tagging.
UR - http://www.scopus.com/inward/record.url?scp=84877737328&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84877737328&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781627480031
VL - 1
SP - 404
EP - 412
BT - Advances in Neural Information Processing Systems
T2 - 26th Annual Conference on Neural Information Processing Systems (NIPS 2012)
Y2 - 3 December 2012 through 6 December 2012
ER -